import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
from torch.autograd import Variable

def conv_bn(in_planes, out_planes, stride, conv_layer = nn.Conv2d, norm_layer = nn.BatchNorm2d, nlin_layer = nn.ReLU):
    return(nn.Sequential(
        conv_layer(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False),
        norm_layer(out_planes),
        nlin_layer(inplace=True)
    ))

def conv_1x1_bn(in_planes, out_planes, conv_layer = nn.Conv2d, norm_layer = nn.BatchNorm2d, nlin = nn.ReLU):
    return(nn.Sequential(
        conv_layer(in_planes, out_planes, kernel_size=1, stride=1, bias=False, padding=0),
        norm_layer(out_planes),
        nlin(inplace=True)
    ))

class Hswish(nn.Module):

    def __init__(self, inplace = True):
        super(Hswish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return x * F.relu6(x + 3., inplace=self.inplace) / 6.


class Hsigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(Hsigmoid, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return F.relu6(x + 3., inplace=self.inplace) / 6.

class SEModule(nn.Module):
    def __init__(self, channel, reduction=4):
        super(SEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            Hsigmoid()
            # nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y.expand_as(x)


class Identity(nn.Module):
    def __init__(self, channel):
        super(Identity, self).__init__()

    def forward(self, x):
        return x

def make_divisible(x, divisible_by = 8):
    import numpy as np
    return int(np.ceil(x * 1. / divisible_by) * divisible_by)


class MobileBottleneck(nn.Module):
    def __init__(self, in_planes, out_planes, kernel, stride, exp, se = False, nl = 'RE'):
        super(MobileBottleneck, self).__init__()
        assert stride in [1, 2]
        assert kernel in [3, 5]
        padding = (kernel - 1) // 2
        self.use_res_connect = stride == 1 and in_planes == out_planes

        conv_layer = nn.Conv2d
        norm_layer = nn.BatchNorm2d
        if nl == 'RE':
            nlin_layer = nn.ReLU
        elif nl == 'HS':
            nlin_layer = Hswish
        else:
            raise NotImplementedError

        if se:
            SELayer = SEModule
        else:
            SELayer = Identity

        self.conv = nn.Sequential(
            # point wise
            conv_layer(in_planes, exp, kernel_size=1, stride=1, padding=0, bias=False),
            norm_layer(exp),
            nlin_layer(inplace=True),
            # depth wise
            conv_layer(exp, exp, kernel_size=kernel, stride=stride, padding=padding, groups=exp, bias=False),
            norm_layer(exp),
            SELayer(exp),
            nlin_layer(inplace=True),
            # point-wise linear
            conv_layer(exp, out_planes, kernel_size=1, stride=1, padding=0, bias=False),
            norm_layer(out_planes)
        )

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MobileNetV3(nn.Module):
    def __init__(self, num_classes, input_size=224, dropout=0.8, mode='small', width_mult = 1.0):
        super(MobileNetV3, self).__init__()
        input_channel = 16
        last_channel = 1280

        if mode == 'large':
            mobile_setting = [
                # k, exp, c,  se,     nl,  s,
                [3, 16, 16, False, 'RE', 1],
                [3, 64, 24, False, 'RE', 2],
                [3, 72, 24, False, 'RE', 1],
                [5, 72, 40, True, 'RE', 2],
                [5, 120, 40, True, 'RE', 1],
                [5, 120, 40, True, 'RE', 1],
                [3, 240, 80, False, 'HS', 2],
                [3, 200, 80, False, 'HS', 1],
                [3, 184, 80, False, 'HS', 1],
                [3, 184, 80, False, 'HS', 1],
                [3, 480, 112, True, 'HS', 1],
                [3, 672, 112, True, 'HS', 1],
                [5, 672, 160, True, 'HS', 2],
                [5, 960, 160, True, 'HS', 1],
                [5, 960, 160, True, 'HS', 1],
            ]

        elif mode == 'small':
            # refer to Table 2 in paper
            mobile_setting = [
                # k, exp, c,  se,     nl,  s,
                [3, 16, 16, True, 'RE', 2],
                [3, 72, 24, False, 'RE', 2],
                [3, 88, 24, False, 'RE', 1],
                [5, 96, 40, True, 'HS', 2],
                [5, 240, 40, True, 'HS', 1],
                [5, 240, 40, True, 'HS', 1],
                [5, 120, 48, True, 'HS', 1],
                [5, 144, 48, True, 'HS', 1],
                [5, 288, 96, True, 'HS', 2],
                [5, 576, 96, True, 'HS', 1],
                [5, 576, 96, True, 'HS', 1],
                ]
        else:
            raise NotImplementedError


        # the first layer
        assert input_size % 32 == 0
        last_channel = make_divisible(last_channel * width_mult) if width_mult > 1.0 else last_channel
        self.features = [conv_bn(3, input_channel, stride=2, nlin_layer=Hswish)]
        self.classfier = []

        # building mobile-bottleneck blocks
        for k, exp, c, se, nl, s in mobile_setting:
            output_channel = make_divisible(c * width_mult)
            exp_channel = make_divisible(exp * width_mult)
            self.features.append(MobileBottleneck(input_channel, output_channel, k, s, exp_channel, se, nl))
            input_channel = output_channel

        # building last several layers
        if mode == 'large':
            last_conv = make_divisible(960 * width_mult)
            self.features.append(conv_1x1_bn(input_channel, last_conv, nlin=Hswish))
            self.features.append(nn.AdaptiveAvgPool2d(1))
            self.features.append(nn.Conv2d(last_conv, last_channel, 1, 1, 0))
            self.features.append(Hswish(inplace=True))
        elif mode == 'small':
            last_conv = make_divisible(576 * width_mult)
            self.features.append(conv_1x1_bn(input_channel, last_conv, nlin=Hswish))
            self.features.append(nn.AdaptiveAvgPool2d(1))
            self.features.append(nn.Conv2d(last_conv, last_channel, 1, 1, 0))
            self.features.append(Hswish(inplace=True))
        else:
            raise NotImplementedError

        # make it nn.Sequential
        self.features = nn.Sequential(*self.features)
        self.classfier = nn.Sequential(
            nn.Dropout(p = dropout),
            nn.Linear(last_channel, num_classes)
        )
        self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x = x.mean(3).mean(2)
        x =self.classfier(x)
        return x
    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode = 'fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            if isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)

def mobilenetv3(pretrained = False, **kwargs):
    model = MobileNetV3(**kwargs)
    if pretrained:
        state_dict = torch.load('')
        model.load_state_dict(state_dict, strict=True)
    return model

if __name__ == '__main__':

    net = MobileNetV3(num_classes=10)
    net.cuda()
    img = torch.rand((1, 3, 224, 224), dtype=torch.float32).cuda()
    t = Variable(img, requires_grad = False)
    with torch.no_grad():
        net.eval()
        out = net(t)
        ps = torch.exp(out)
        ps = ps / torch.sum(ps)
        print(ps)











